


'''simple docstring'''






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptTokenizer,
		)
		from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=7   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=False   ,       lowerCamelCase_=True   ,       lowerCamelCase_=99   ,       lowerCamelCase_=32   ,       lowerCamelCase_=5   ,       lowerCamelCase_=4   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=5_12   ,       lowerCamelCase_=16   ,       lowerCamelCase_=2   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=3   ,       lowerCamelCase_=4   ,       lowerCamelCase_=None   ,       )			->				List[str]:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      seq_length
														lowerCAmelCase__			  =      is_training
														lowerCAmelCase__			  =      use_input_mask
														lowerCAmelCase__			  =      use_token_type_ids
														lowerCAmelCase__			  =      use_labels
														lowerCAmelCase__			  =      vocab_size
														lowerCAmelCase__			  =      hidden_size
														lowerCAmelCase__			  =      num_hidden_layers
														lowerCAmelCase__			  =      num_attention_heads
														lowerCAmelCase__			  =      intermediate_size
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      hidden_dropout_prob
														lowerCAmelCase__			  =      attention_probs_dropout_prob
														lowerCAmelCase__			  =      max_position_embeddings
														lowerCAmelCase__			  =      type_vocab_size
														lowerCAmelCase__			  =      type_sequence_label_size
														lowerCAmelCase__			  =      initializer_range
														lowerCAmelCase__			  =      num_labels
														lowerCAmelCase__			  =      num_choices
														lowerCAmelCase__			  =      scope







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size  )

														lowerCAmelCase__			  =      None
														if self.use_input_mask:
																					lowerCAmelCase__			  =      random_attention_mask([self.batch_size, self.seq_length]  )

														lowerCAmelCase__			  =      None
														if self.use_token_type_ids:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size  )

														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      None
														if self.use_labels:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.type_sequence_label_size  )
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels  )
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.num_choices  )

														lowerCAmelCase__			  =      self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return BioGptConfig(
														    vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       is_decoder=lowerCamelCase_   ,       initializer_range=self.initializer_range   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      BioGptModel(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  )
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       )			->				Optional[Any]:
														lowerCAmelCase__			  =      BioGptForCausalLM(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_   ,       token_type_ids=lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				str:
														lowerCAmelCase__			  =      BioGptModel(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()

														# create attention mask
														lowerCAmelCase__			  =      torch.ones(input_ids.shape   ,       dtype=torch.long   ,       device=lowerCamelCase_  )
														lowerCAmelCase__			  =      self.seq_length // 2
														lowerCAmelCase__			  =      0

														# first forward pass
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  ).to_tuple()

														# create hypothetical next token and extent to next_input_ids
														lowerCAmelCase__			  =      ids_tensor((self.batch_size, 1)   ,       config.vocab_size  )

														# change a random masked slice from input_ids
														lowerCAmelCase__			  =      ids_tensor((1,)   ,       lowerCamelCase_  ).item() + 1
														lowerCAmelCase__			  =      ids_tensor((self.batch_size, 1)   ,       config.vocab_size  ).squeeze(-1  )
														lowerCAmelCase__			  =      random_other_next_tokens

														# append to next input_ids and attn_mask
														lowerCAmelCase__			  =      torch.cat([input_ids, next_tokens]   ,       dim=-1  )
														lowerCAmelCase__			  =      torch.cat(
														    [attn_mask, torch.ones((attn_mask.shape[0], 1)   ,       dtype=torch.long   ,       device=lowerCamelCase_  )]   ,       dim=1   ,       )

														# get two different outputs
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  )['''last_hidden_state''']
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       past_key_values=lowerCamelCase_   ,       attention_mask=lowerCamelCase_  )['''last_hidden_state''']

														# select random slice
														lowerCAmelCase__			  =      ids_tensor((1,)   ,       output_from_past.shape[-1]  ).item()
														lowerCAmelCase__			  =      output_from_no_past[:, -1, random_slice_idx].detach()
														lowerCAmelCase__			  =      output_from_past[:, 0, random_slice_idx].detach()

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(lowerCamelCase_   ,       lowerCamelCase_   ,       atol=1e-3  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				Tuple:
														lowerCAmelCase__			  =      BioGptModel(config=lowerCamelCase_  ).to(lowerCamelCase_  ).eval()

														lowerCAmelCase__			  =      torch.ones(input_ids.shape   ,       dtype=torch.long   ,       device=lowerCamelCase_  )

														# first forward pass
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_   ,       use_cache=lowerCamelCase_  )

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      outputs.to_tuple()

														# create hypothetical multiple next token and extent to next_input_ids
														lowerCAmelCase__			  =      ids_tensor((self.batch_size, 3)   ,       config.vocab_size  )
														lowerCAmelCase__			  =      ids_tensor((self.batch_size, 3)   ,       2  )

														# append to next input_ids and
														lowerCAmelCase__			  =      torch.cat([input_ids, next_tokens]   ,       dim=-1  )
														lowerCAmelCase__			  =      torch.cat([attention_mask, next_attn_mask]   ,       dim=-1  )

														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_  )['''last_hidden_state''']
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_   ,       past_key_values=lowerCamelCase_  )[
														    '''last_hidden_state'''
														]

														# select random slice
														lowerCAmelCase__			  =      ids_tensor((1,)   ,       output_from_past.shape[-1]  ).item()
														lowerCAmelCase__			  =      output_from_no_past[:, -3:, random_slice_idx].detach()
														lowerCAmelCase__			  =      output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(lowerCamelCase_   ,       lowerCamelCase_   ,       atol=1e-3  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_   ,       lowerCamelCase_=False  )			->				List[Any]:
														lowerCAmelCase__			  =      BioGptForCausalLM(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														if gradient_checkpointing:
																					model.gradient_checkpointing_enable()

														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(result.loss.shape   ,       ()  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)  )
														result.loss.backward()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      BioGptModel(lowerCamelCase_  )
														lowerCAmelCase__			  =      model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers  )
														for key in model.state_dict().keys():
																					if "c_proj" in key and "weight" in key:
																												self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]  ) - model_std  )   ,       0.001  )
																												self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]  ) - 0.0  )   ,       0.01  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       *lowerCamelCase_  )			->				str:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      BioGptForTokenClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_   ,       token_type_ids=lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														(
														    (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   
														)			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
														return config, inputs_dict




@require_torch
class 						a__							(				a__							,							a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  List[Any]      		= (
							    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
							    if is_torch_available()
							    else ()
							)
							lowercase__     :  Any      		= (BioGptForCausalLM,) if is_torch_available() else ()
							lowercase__     :  Optional[Any]      		= (
							    {
							        "feature-extraction": BioGptModel,
							        "text-classification": BioGptForSequenceClassification,
							        "text-generation": BioGptForCausalLM,
							        "token-classification": BioGptForTokenClassification,
							        "zero-shot": BioGptForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase__     :  Tuple      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      BioGptModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(self   ,       config_class=lowerCamelCase_   ,       hidden_size=37  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														self.config_tester.run_common_tests()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					lowerCAmelCase__			  =      type
																					self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase_   ,       gradient_checkpointing=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''  )
														model.to(lowerCamelCase_  )
														lowerCAmelCase__			  =      BioGptTokenizer.from_pretrained('''microsoft/biogpt'''  )

														lowerCAmelCase__			  =      '''left'''

														# Define PAD Token = EOS Token = 50256
														lowerCAmelCase__			  =      tokenizer.eos_token
														lowerCAmelCase__			  =      model.config.eos_token_id

														# use different length sentences to test batching
														lowerCAmelCase__			  =      [
														    '''Hello, my dog is a little''',
														    '''Today, I''',
														]

														lowerCAmelCase__			  =      tokenizer(lowerCamelCase_   ,       return_tensors='''pt'''   ,       padding=lowerCamelCase_  )
														lowerCAmelCase__			  =      inputs['''input_ids'''].to(lowerCamelCase_  )

														lowerCAmelCase__			  =      model.generate(
														    input_ids=lowerCamelCase_   ,       attention_mask=inputs['''attention_mask'''].to(lowerCamelCase_  )   ,       )

														lowerCAmelCase__			  =      tokenizer(sentences[0]   ,       return_tensors='''pt'''  ).input_ids.to(lowerCamelCase_  )
														lowerCAmelCase__			  =      model.generate(input_ids=lowerCamelCase_  )

														lowerCAmelCase__			  =      inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
														lowerCAmelCase__			  =      tokenizer(sentences[1]   ,       return_tensors='''pt'''  ).input_ids.to(lowerCamelCase_  )
														lowerCAmelCase__			  =      model.generate(input_ids=lowerCamelCase_   ,       max_length=model.config.max_length - num_paddings  )

														lowerCAmelCase__			  =      tokenizer.batch_decode(lowerCamelCase_   ,       skip_special_tokens=lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.decode(output_non_padded[0]   ,       skip_special_tokens=lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.decode(output_padded[0]   ,       skip_special_tokens=lowerCamelCase_  )

														lowerCAmelCase__			  =      [
														    '''Hello, my dog is a little bit bigger than a little bit.''',
														    '''Today, I have a good idea of how to use the information''',
														]
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       [non_padded_sentence, padded_sentence]  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__			  =      BioGptModel.from_pretrained(lowerCamelCase_  )
																					self.assertIsNotNone(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__			  =      3
														lowerCAmelCase__			  =      input_dict['''input_ids''']
														lowerCAmelCase__			  =      input_ids.ne(1  ).to(lowerCamelCase_  )
														lowerCAmelCase__			  =      ids_tensor([self.model_tester.batch_size]   ,       self.model_tester.type_sequence_label_size  )
														lowerCAmelCase__			  =      BioGptForSequenceClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.assertEqual(result.logits.shape   ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__			  =      3
														lowerCAmelCase__			  =      '''multi_label_classification'''
														lowerCAmelCase__			  =      input_dict['''input_ids''']
														lowerCAmelCase__			  =      input_ids.ne(1  ).to(lowerCamelCase_  )
														lowerCAmelCase__			  =      ids_tensor(
														    [self.model_tester.batch_size, config.num_labels]   ,       self.model_tester.type_sequence_label_size  ).to(torch.float  )
														lowerCAmelCase__			  =      BioGptForSequenceClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       attention_mask=lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.assertEqual(result.logits.shape   ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )




@require_torch
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''  )
														lowerCAmelCase__			  =      torch.tensor([[2, 48_05, 9, 6_56, 21]]  )
														lowerCAmelCase__			  =      model(lowerCamelCase_  )[0]

														lowerCAmelCase__			  =      4_23_84

														lowerCAmelCase__			  =      torch.Size((1, 5, vocab_size)  )
														self.assertEqual(output.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.tensor(
														    [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]  )

														self.assertTrue(torch.allclose(output[:, :3, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      BioGptTokenizer.from_pretrained('''microsoft/biogpt'''  )
														lowerCAmelCase__			  =      BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''  )
														model.to(lowerCamelCase_  )

														torch.manual_seed(0  )
														lowerCAmelCase__			  =      tokenizer('''COVID-19 is'''   ,       return_tensors='''pt'''  ).to(lowerCamelCase_  )
														lowerCAmelCase__			  =      model.generate(
														    **lowerCamelCase_   ,       min_length=1_00   ,       max_length=10_24   ,       num_beams=5   ,       early_stopping=lowerCamelCase_   ,       )
														lowerCAmelCase__			  =      tokenizer.decode(output_ids[0]   ,       skip_special_tokens=lowerCamelCase_  )

														lowerCAmelCase__			  =      (
														    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
														    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
														    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
														    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
														    ''' more than 800,000 deaths.'''
														)
														self.assertEqual(lowerCamelCase_   ,       lowerCamelCase_  )




'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




